Efficient Picking by Considering Simultaneous Two-Object Grasping

Abstract

This paper presents a motion planning algorithm that enables robots to efficiently pick up objects by considering simultaneous multi-object grasping. At the center of the algorithm is a cost function that helps to determine one of the following three grasping policies considering distance and friction constraints – Grasping a single object; Grasping two objects simultaneously; Grasping two object simultaneously after pushing one of the objects close to the other. After recognizing the object distributions on a table by using a depth camera and Mask R-CNN, our algorithm will select grasp policies from the three candidates considering the cost function, and plan a policy sequence that can most quickly finish picking all the objects using dynamic programming. Both simulation and real-world experiments are carried out to examine the performance of the proposed planner. Results show that the proposed method significantly improves the efficiency of robotic picking compared to conventional single-object-based methods.